Role of audiovisual synchrony in driving head orienting responses

On the Timing of Signals in Multisensory Integration and Crossmodal Interactions: a Scoping Review

A scoping review was undertaken to explore research investigating early interactions and integration of auditory and visual stimuli in the human brain. The focus was on methods used to study low-level multisensory temporal processing using simple stimuli in humans, and how this research has informed our understanding of multisensory perception. The study of multisensory temporal processing probes how the relative timing between signals affects perception. Several tasks, illusions, computational models, and neuroimaging techniques were identified in the literature search. Research into early audiovisual temporal processing in special populations was also reviewed. Recent research has continued to provide support for early integration of crossmodal information. These early interactions can influence higher-level factors, and vice versa. Temporal relationships between auditory and visual stimuli influence multisensory perception, and likely play a substantial role in solving the 'correspondence problem' (how the brain determines which sensory signals belong together, and which should be segregated).

Crowdsourced Measurement of Reaction Times to Audiovisual Stimuli With Various Degrees of Asynchrony

OBJECTIVE

This study was designed to replicate past research concerning reaction times to audiovisual stimuli with different stimulus onset asynchrony (SOA) using a large sample of crowdsourcing respondents.

BACKGROUND

Research has shown that reaction times are fastest when an auditory and a visual stimulus are presented simultaneously and that SOA causes an increase in reaction time, this increase being dependent on stimulus intensity. Research on audiovisual SOA has been conducted with small numbers of participants.

METHOD

Participants ( N = 1,823) each performed 176 reaction time trials consisting of 29 SOA levels and three visual intensity levels, using CrowdFlower, with a compensation of US$0.20 per participant. Results were verified with a local Web-in-lab study ( N = 34).

RESULTS

The results replicated past research, with a V shape of mean reaction time as a function of SOA, the V shape being stronger for lower-intensity visual stimuli. The level of SOA affected mainly the right side of the reaction time distribution, whereas the fastest 5% was hardly affected. The variability of reaction times was higher for the crowdsourcing study than for the Web-in-lab study.

CONCLUSION

Crowdsourcing is a promising medium for reaction time research that involves small temporal differences in stimulus presentation. The observed effects of SOA can be explained by an independent-channels mechanism and also by some participants not perceiving the auditory or visual stimulus, hardware variability, misinterpretation of the task instructions, or lapses in attention.

APPLICATION

The obtained knowledge on the distribution of reaction times may benefit the design of warning systems.

Behavioural benefits of multisensory processing in ferrets

Enhanced detection and discrimination, along with faster reaction times, are the most typical behavioural manifestations of the brain's capacity to integrate multisensory signals arising from the same object. In this study, we examined whether multisensory behavioural gains are observable across different components of the localization response that are potentially under the command of distinct brain regions. We measured the ability of ferrets to localize unisensory (auditory or visual) and spatiotemporally coincident auditory-visual stimuli of different durations that were presented from one of seven locations spanning the frontal hemifield. During the localization task, we recorded the head movements made following stimulus presentation, as a metric for assessing the initial orienting response of the ferrets, as well as the subsequent choice of which target location to approach to receive a reward. Head-orienting responses to auditory-visual stimuli were more accurate and faster than those made to visual but not auditory targets, suggesting that these movements were guided principally by sound alone. In contrast, approach-to-target localization responses were more accurate and faster to spatially congruent auditory-visual stimuli throughout the frontal hemifield than to either visual or auditory stimuli alone. Race model inequality analysis of head-orienting reaction times and approach-to-target response times indicates that different processes, probability summation and neural integration, respectively, are likely to be responsible for the effects of multisensory stimulation on these two measures of localization behaviour.

Audiovisual Integration of Time-to-Contact Information for Approaching Objects

Previous studies of time-to-collision (TTC) judgments of approaching objects focused on effectiveness of visual TTC information in the optical expansion pattern (e.g., visual tau, disparity). Fewer studies examined effectiveness of auditory TTC information in the pattern of increasing intensity (auditory tau), or measured integration of auditory and visual TTC information. Here, participants judged TTC of an approaching object presented in the visual or auditory modality, or both concurrently. TTC information provided by the modalities was jittered slightly against each other, so that auditory and visual TTC were not perfectly correlated. A psychophysical reverse correlation approach was used to estimate the influence of auditory and visual cues on TTC estimates. TTC estimates were shorter in the auditory than the visual condition. On average, TTC judgments in the audiovisual condition were not significantly different from judgments in the visual condition. However, multiple regression analyses showed that TTC estimates were based on both auditory and visual information. Although heuristic cues (final sound pressure level, final optical size) and more reliable information (relative rate of change in acoustic intensity, optical expansion) contributed to auditory and visual judgments, the effect of heuristics was greater in the auditory condition. Although auditory and visual information influenced judgments, concurrent presentation of both did not result in lower response variability compared to presentation of either one alone; there was no multimodal advantage. The relative weightings of heuristics and more reliable information differed between auditory and visual TTC judgments, and when both were available, visual information was weighted more heavily.

Designing driver assistance systems with crossmodal signals: multisensory integration rules for saccadic reaction times apply

Modern driver assistance systems make increasing use of auditory and tactile signals in order to reduce the driver's visual information load. This entails potential crossmodal interaction effects that need to be taken into account in designing an optimal system. Here we show that saccadic reaction times to visual targets (cockpit or outside mirror), presented in a driving simulator environment and accompanied by auditory or tactile accessories, follow some well-known spatiotemporal rules of multisensory integration, usually found under confined laboratory conditions. Auditory nontargets speed up reaction time by about 80 ms. The effect tends to be maximal when the nontarget is presented 50 ms before the target and when target and nontarget are spatially coincident. The effect of a tactile nontarget (vibrating steering wheel) was less pronounced and not spatially specific. It is shown that the average reaction times are well-described by the stochastic "time window of integration" model for multisensory integration developed by the authors. This two-stage model postulates that crossmodal interaction occurs only if the peripheral processes from the different sensory modalities terminate within a fixed temporal interval, and that the amount of crossmodal interaction manifests itself in an increase or decrease of second stage processing time. A qualitative test is consistent with the model prediction that the probability of interaction, but not the amount of crossmodal interaction, depends on target-nontarget onset asynchrony. A quantitative model fit yields estimates of individual participants' parameters, including the size of the time window. Some consequences for the design of driver assistance systems are discussed.